Comparing and contrasting different algorithms leads to increased student learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Comparing and contrasting different solution approaches is known in math education and cognitive science to increase student learning -- what about CS? In this experiment, we replicated work from Rittle-Johnson and Star, using a pretest--intervention--posttest--follow-up design (n=241). Our intervention was an in-class workbook in CS2. A randomized half of students received questions in a compare-and-contrast style, seeing different code for different algorithms in parallel. The other half saw the same code questions sequentially, and evaluated them one at a time. Students in the former group performed better with regard to procedural knowledge (code reading & writing), and flexibility (generating, recognizing & evaluating multiple ways to solve a problem). The two groups performed equally on conceptual knowledge. Our results agree with those of Rittle-Johnson and Star, indicating that the existing work in this area generalizes to CS education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it